Connecting IBM DB2 to Snowflake: 3 Easy Steps

In today’s commercial world, compiling and making large data sets available for inquiries and internal reporting is vital. A short search of the database yields near-instant results based on an analysis of potentially millions of sources. As your data collection develops, you will want a sophisticated automation process, this is where connecting IBM DB2 to Snowflake can help.

IBM DB2 is designed to store, analyze, and retrieve data efficiently. Snowflake works with several technology partners to help move data from a source database to an object storage destination that can be utilized as an external stage.

This tutorial will teach you how to set up an IBM DB2 to Snowflake Connection. We will lead you through each step necessary to arrive at the best option. Let’s get started.

What is IBM DB2?

DB2 is an IBM data management product family that includes database servers. They initially supported the relational model but were later expanded to handle object-relational features as well as non-relational structures such as JSON and XML. The brand name was originally DB/2, then DB2 until 2017, when it was ultimately transformed to its current form.

Unlike other database providers, IBM used to produce a platform-specific Db2 solution for each of its major operating systems. However, in the 1990s, IBM shifted course and created a Db2 common product with a generally common code base for L-U-W (Linux-Unix-Windows); DB2 for System z and DB2 for IBM I are distinct. As a result, they employ several drivers.

Key Features of IBM DB2

The following are some of IBM DB2’s key features:

• Acceleration of IBM BLU: For processing columnar data, IBM DB2 includes in-memory technology. It also offers an efficient RAM access procedure and extra features such as Actionable Compression, CPU Acceleration, and Data Skipping.
• Optimization of Storage: Data compression and data skipping techniques dramatically reduce power usage in DB2.
• Improved Performance: IBM DB2 can automatically manage and schedule workload execution using AI approaches to deliver excellent performance.
• Scalability: In multi-cloud and hybrid environment setups, IBM DB2 can expand up to 128 computers to reduce storage expenses.

What is Snowflake?

Snowflake is a fully managed Cloud-based Data Warehouse that is available as Software-as-a-Service (SaaS). Its fully managed capabilities eliminate the need for you to do back-end operations such as server installation, maintenance, and so on. Furthermore, a Snowflake Data Warehouse instance can be easily deployed on any of the main cloud providers, including Amazon Web Services (AWS), Google Cloud Storage (GCS), and Microsoft Azure.

Snowflake’s flexible storage can hold virtually an infinite amount of organized and semi-structured data. This allows you to combine data from all of your sources into a single area. Furthermore, without making any additional purchases, you can quickly scale our virtual Data Warehouse to match your growing business needs.

Key Features of Snowflake

The following are some of Snowflake’s key features:

• Workload division: Snowflake employs a multi-cluster architecture to address concurrency difficulties. It also allows you to divide the burden among its multiple clusters. This allows you to conduct queries in parallel without interfering with other clusters.
• Security: Snowflake offers a plethora of security measures. You can change its network settings and whitelist IP addresses to prevent account access.
• Scalability: The multi-cluster architecture of Snowflake allows you to have independent computation and storage resources. This allows you to increase resources to suit business development without interfering with the work of other projects.

IBM DB2 to Snowflake Migration Plan (Manual)

The data flow for migrating any source system to Snowflake may be reduced to the three-box architecture shown below.

This image instantly inserts a brief halt for the source data in an ‘external stage.’ This is due to Snowflake’s lack of native methods for obtaining data directly from a database. Instead, it employs the idea of a Stage, which can be internal or external, as a location from which to load and unload data.

Snowflake Stages are simply pointers to public cloud object storage locations that also provide metadata stored there. These object storage sites are ideal for staging data since they can grow indefinitely, and big corporations will commonly build data lakes over this storage, making it easy to transfer from IBM DB2 to Snowflake and allowing data in Snowflake to be rapidly added back to the lake.

This migration, on the other hand, will concentrate on an AWS cloud-native solution, the Database Migration Service (DMS).

1) Source

It is a data migration service that allows highly customized data migrations and replications between numerous sources and targets. Because DB2 is a readily available source system, this is the ideal tool for data collecting IBM DB2 to Snowflake. Targets include Apache Kafka, S3, DynamoDB, and Kinesis, however, for Snowflake consumption, we will focus on S3.

AWS DMS is a SaaS service that is mostly configured by the user. Its replication duties include options for bulk load movement and CDC for prospective data. By leveraging DB2’s native API to read archived logs as they are written, the replication jobs achieve near-real-time latency for CDC. It is also critical to ensure that the DMS infrastructure can access the DB2 database, as well as that the source database can provide the necessary data.

2) External Stage

Snowflake is a SaaS service that uses SQL commands to construct data warehouse systems. Snowflake charges for storage and computation on a per-second basis.

Snowflake’s pipe and task objects help you design low-latency data pipelines. Furthermore, data landing in S3 can be processed the same way across the Snowflake pipeline, whether retrospective or prospective. Constructing this stage of the migration involves setting a few components to provide authorized access to the data in S3 and to assure fast delivery to the Snowflake pipe.

In this Migration, two tables are involved in this stage of the transfer. The first is a data representation that is identical to what is in DB2, an append-only table that includes the actions conducted upon the source database, known as the ‘change table.’ The second reflects the (largely) current state of the source database, the ‘reporting table,’ which is the data’s eventual destination in the migration.

The Snowpipe pipe is used to keep the changing table up to date with the most recent data from DB2. A pipe is just a COPY INTO statement that monitors data changes in a stage. 

The pipe queues its COPY INTO command for a Snowflake-managed warehouse to copy the data into the changing table when it receives a signal.

• Table Streams track DML changes to a table and allow action to be done on the delta. The stream on the changing table keeps track of changes to the changing table, which will only be insert operations for this migration because the data arriving from S3 is either bulk load data or CDC data from the source database.
• Tasks are the scheduled execution of a SQL query. Creating a task necessitates the provision of a warehouse from which the query will be executed. This step’s migration employs a job that runs on a regular basis, checks the stream for any changes, and then executes a MERGE INTO statement on the reporting table.

These Snowflake resources work together to reconstruct the CDC data as it arrives in S3 to a representation that mirrors the source, with some delay.

3) Snowflake

Using templates to create DDL statements is an excellent way to apply conventional naming rules to Snowflake objects. All the Snowflake tables, pipes, jobs, and streams are part of a Snowflake schema, and all Snowflake schemas are part of a Snowflake database, it makes sense to use the same database/schema set up as the source databases when deciding where to store the Snowflake components.

The Snowflake components are substantially simpler, as the pipeline elements are stamped out only once for each table that is being migrated. The authorization and stage referenced components will be generally reusable across the various pipelines.

Connecting IBM DB2 to Snowflake

This page contains methods for extracting data from Db2 and loading it into Snowflake.

• Step 1: Getting data from DB2
• Step 2: Data preparation for Snowflake
  • Step 2.1: Data loading in IBM DB2 to Snowflake
• Step 3: Keeping DB2 Upto Date

Step 1: Getting data from DB2

Setting up a source database to be correctly utilized by DMS necessitates a bit more configuration than simply giving it to a user. In general, the following must be set up:

• For a one-time batch, it is usually sufficient to allow the database user to query the tables that need to be moved.

• Ongoing replication necessitates database-specific activities to allow data collection and offer access to the database user.

For IBM DB2 to Snowflake, continuing replication would be handled by permitting the database to recoverable logs (through LOGARCHMETH1 or LOGARCHMETH2), persisting them for a suitable length of time, and allowing the database user to access them.

SELECT queries are the most frequent approach to extracting data from a relational database. You can limit results by setting filters and sorting. To export data from an entire table, use the EXPORT command.

Step 2: Data preparation for Snowflake

You may need to prepare your data before loading it, depending on your data structures. Check the Snowflake-supported data types to ensure that your data maps smoothly to them.

When loading JSON or XML data into Snowflake, you will not need to create a schema in preparation.

Step 2.1: Data loading in IBM DB2 to Snowflake

For assistance with data loading, consult Snowflake’s Data Loading Overview. You might be able to use Snowflake’s data loading wizard if you’re not loading a lot of data, but its restrictions make it unsuitable as a dependable ETL solution for several use scenarios. You have the option of:

• To stage files, use the PUT command.
• To load prepared data into an awaiting table, use the COPY INTO table command.

You’ll be able to copy from your local drive or Amazon S3 – and Snowflake allows you to create a virtual warehouse to fuel the insertion operation.

Step 3: Keeping DB2 Upto Date

So you’ve created a script that will export data from IBM DB2 to Snowflake. That should cover all of your DB2 data requirements, right? No, not yet. How do new or updated data get loaded? It is not a good idea to replicate all of your data every time your records are modified. That process would be very slow; if latency is crucial to you, this is not a choice.

Instead, identify some important fields that your script may utilize to save its progress through the data and resume where it left off when it searches for updated data. Auto-incrementing fields like updated at and created at are ideal for this. After you’ve implemented this capability, you may run your script as a cron job or in a continuous loop to retrieve new data from DB2.

Benefits of Connecting IBM DB2 to Snowflake

Here are some benefits of connecting IBM DB2 to Snowflake:

• Query data from S3, Blob, JSON, and XML as a table: IBM DB2 to Snowflake lets you automatically normalize API/JSON/XML/S3/Blob/NoSql sources into the ready-to-query relational format, you may eliminate typical ETL/EDW bottlenecks. 
• Gain centralized access to all of your data: With the ready-to-use connectors, you may access data from over 200 data sources and replicate it to your central data warehouse.
• Allow for data-driven decision-making: IBM DB2 to Snowflake connection provides uniform and standardized data to everyone in your organization, automates data delivery, and tracks KPIs across many systems.

Conclusion

This post showed you how to configure an IBM DB2 to Snowflake connection. And guided you through every step required to arrive at the finest decision.

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